Overview

Dataset statistics

Number of variables9
Number of observations767
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory54.1 KiB
Average record size in memory72.2 B

Variable types

Numeric8
Categorical1

Warnings

Pregnancies has 111 (14.5%) zeros Zeros

Reproduction

Analysis started2021-04-20 06:07:48.861831
Analysis finished2021-04-20 06:08:07.556386
Duration18.69 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Pregnancies
Real number (ℝ≥0)

ZEROS

Distinct17
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.848761408
Minimum0
Maximum17
Zeros111
Zeros (%)14.5%
Memory size6.1 KiB
2021-04-20T11:53:07.794502image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.370207423
Coefficient of variation (CV)0.8756602619
Kurtosis0.1563476434
Mean3.848761408
Median Absolute Deviation (MAD)2
Skewness0.8998253832
Sum2952
Variance11.35829807
MonotocityNot monotonic
2021-04-20T11:53:07.904378image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1134
17.5%
0111
14.5%
2103
13.4%
375
9.8%
468
8.9%
557
7.4%
650
 
6.5%
745
 
5.9%
838
 
5.0%
928
 
3.7%
Other values (7)58
7.6%
ValueCountFrequency (%)
0111
14.5%
1134
17.5%
2103
13.4%
375
9.8%
468
8.9%
ValueCountFrequency (%)
171
 
0.1%
151
 
0.1%
142
 
0.3%
1310
1.3%
129
1.2%

Glucose
Real number (ℝ≥0)

Distinct136
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.7192366
Minimum44
Maximum199
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2021-04-20T11:53:08.042725image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile80
Q1100
median117
Q3140.5
95-th percentile181
Maximum199
Range155
Interquartile range (IQR)40.5

Descriptive statistics

Standard deviation30.43821062
Coefficient of variation (CV)0.2500690232
Kurtosis-0.2596951528
Mean121.7192366
Median Absolute Deviation (MAD)20
Skewness0.5311836305
Sum93358.6545
Variance926.4846655
MonotocityNot monotonic
2021-04-20T11:53:08.188516image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9917
 
2.2%
10017
 
2.2%
12914
 
1.8%
10614
 
1.8%
12514
 
1.8%
11114
 
1.8%
10813
 
1.7%
9513
 
1.7%
11213
 
1.7%
10213
 
1.7%
Other values (126)625
81.5%
ValueCountFrequency (%)
441
0.1%
561
0.1%
572
0.3%
611
0.1%
621
0.1%
ValueCountFrequency (%)
1991
 
0.1%
1981
 
0.1%
1974
0.5%
1963
0.4%
1952
0.3%

BloodPressure
Real number (ℝ≥0)

Distinct47
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.25769307
Minimum24
Maximum122
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2021-04-20T11:53:08.347173image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile52
Q164
median72
Q380
95-th percentile90
Maximum122
Range98
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.12357736
Coefficient of variation (CV)0.1677825135
Kurtosis1.074110209
Mean72.25769307
Median Absolute Deviation (MAD)8
Skewness0.1722404697
Sum55421.65059
Variance146.981128
MonotocityNot monotonic
2021-04-20T11:53:08.487236image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
7056
 
7.3%
7452
 
6.8%
7845
 
5.9%
6845
 
5.9%
7244
 
5.7%
6443
 
5.6%
8040
 
5.2%
7639
 
5.1%
6037
 
4.8%
69.1043024835
 
4.6%
Other values (37)331
43.2%
ValueCountFrequency (%)
241
 
0.1%
302
0.3%
381
 
0.1%
401
 
0.1%
444
0.5%
ValueCountFrequency (%)
1221
 
0.1%
1141
 
0.1%
1103
0.4%
1082
0.3%
1063
0.4%

SkinThickness
Real number (ℝ≥0)

Distinct51
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.59671352
Minimum7
Maximum99
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2021-04-20T11:53:08.632235image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile14.3
Q120.52281617
median23
Q332
95-th percentile44
Maximum99
Range92
Interquartile range (IQR)11.47718383

Descriptive statistics

Standard deviation9.638762095
Coefficient of variation (CV)0.362404253
Kurtosis3.897513402
Mean26.59671352
Median Absolute Deviation (MAD)5
Skewness1.22782446
Sum20399.67927
Variance92.90573472
MonotocityNot monotonic
2021-04-20T11:53:08.769555image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.52281617227
29.6%
3231
 
4.0%
3027
 
3.5%
2723
 
3.0%
2322
 
2.9%
1820
 
2.6%
2820
 
2.6%
3320
 
2.6%
3118
 
2.3%
3918
 
2.3%
Other values (41)341
44.5%
ValueCountFrequency (%)
72
 
0.3%
82
 
0.3%
105
0.7%
116
0.8%
127
0.9%
ValueCountFrequency (%)
991
0.1%
631
0.1%
601
0.1%
561
0.1%
542
0.3%

Insulin
Real number (ℝ≥0)

Distinct186
Distinct (%)24.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118.7614251
Minimum14
Maximum846
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2021-04-20T11:53:08.914883image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile50
Q179.90352021
median79.90352021
Q3127.5
95-th percentile293
Maximum846
Range832
Interquartile range (IQR)47.59647979

Descriptive statistics

Standard deviation93.10934221
Coefficient of variation (CV)0.7840032413
Kurtosis14.1282867
Mean118.7614251
Median Absolute Deviation (MAD)3.096479791
Skewness3.290164681
Sum91090.01304
Variance8669.349607
MonotocityNot monotonic
2021-04-20T11:53:09.057702image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79.90352021373
48.6%
10511
 
1.4%
1309
 
1.2%
1409
 
1.2%
1208
 
1.0%
947
 
0.9%
1807
 
0.9%
1007
 
0.9%
1106
 
0.8%
1356
 
0.8%
Other values (176)324
42.2%
ValueCountFrequency (%)
141
0.1%
151
0.1%
161
0.1%
182
0.3%
221
0.1%
ValueCountFrequency (%)
8461
0.1%
7441
0.1%
6801
0.1%
6001
0.1%
5791
0.1%

BMI
Real number (ℝ≥0)

Distinct248
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.45350873
Minimum18.2
Maximum67.1
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2021-04-20T11:53:09.206978image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum18.2
5-th percentile22.23
Q127.5
median32
Q336.6
95-th percentile44.41
Maximum67.1
Range48.9
Interquartile range (IQR)9.1

Descriptive statistics

Standard deviation6.879458326
Coefficient of variation (CV)0.2119788767
Kurtosis0.9161725942
Mean32.45350873
Median Absolute Deviation (MAD)4.5
Skewness0.5996711105
Sum24891.8412
Variance47.32694686
MonotocityNot monotonic
2021-04-20T11:53:09.351781image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3213
 
1.7%
31.212
 
1.6%
31.612
 
1.6%
31.994654511
 
1.4%
32.410
 
1.3%
33.310
 
1.3%
30.89
 
1.2%
32.89
 
1.2%
30.19
 
1.2%
32.99
 
1.2%
Other values (238)663
86.4%
ValueCountFrequency (%)
18.23
0.4%
18.41
 
0.1%
19.11
 
0.1%
19.31
 
0.1%
19.41
 
0.1%
ValueCountFrequency (%)
67.11
0.1%
59.41
0.1%
57.31
0.1%
551
0.1%
53.21
0.1%

DiabetesPedigreeFunction
Real number (ℝ≥0)

Distinct517
Distinct (%)67.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4720808344
Minimum0.078
Maximum2.42
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2021-04-20T11:53:09.507524image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.078
5-th percentile0.1403
Q10.2435
median0.374
Q30.6265
95-th percentile1.1333
Maximum2.42
Range2.342
Interquartile range (IQR)0.383

Descriptive statistics

Standard deviation0.3314962775
Coefficient of variation (CV)0.702202363
Kurtosis5.584011879
Mean0.4720808344
Median Absolute Deviation (MAD)0.169
Skewness1.917791098
Sum362.086
Variance0.109889782
MonotocityNot monotonic
2021-04-20T11:53:09.652503image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2586
 
0.8%
0.2546
 
0.8%
0.2595
 
0.7%
0.2075
 
0.7%
0.2385
 
0.7%
0.2615
 
0.7%
0.2685
 
0.7%
0.2454
 
0.5%
0.3044
 
0.5%
0.1974
 
0.5%
Other values (507)718
93.6%
ValueCountFrequency (%)
0.0781
0.1%
0.0841
0.1%
0.0852
0.3%
0.0882
0.3%
0.0891
0.1%
ValueCountFrequency (%)
2.421
0.1%
2.3291
0.1%
2.2881
0.1%
2.1371
0.1%
1.8931
0.1%

Age
Real number (ℝ≥0)

Distinct52
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.25423729
Minimum21
Maximum81
Zeros0
Zeros (%)0.0%
Memory size6.1 KiB
2021-04-20T11:53:09.959560image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q124
median29
Q341
95-th percentile58
Maximum81
Range60
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.76207916
Coefficient of variation (CV)0.3537016669
Kurtosis0.6397407766
Mean33.25423729
Median Absolute Deviation (MAD)7
Skewness1.127991799
Sum25506
Variance138.3465062
MonotocityNot monotonic
2021-04-20T11:53:10.105876image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2272
 
9.4%
2163
 
8.2%
2548
 
6.3%
2446
 
6.0%
2337
 
4.8%
2835
 
4.6%
2633
 
4.3%
2732
 
4.2%
2929
 
3.8%
3124
 
3.1%
Other values (42)348
45.4%
ValueCountFrequency (%)
2163
8.2%
2272
9.4%
2337
4.8%
2446
6.0%
2548
6.3%
ValueCountFrequency (%)
811
0.1%
721
0.1%
701
0.1%
692
0.3%
681
0.1%

Outcome
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
0
499 
1
268 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters767
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1
ValueCountFrequency (%)
0499
65.1%
1268
34.9%
2021-04-20T11:53:10.353302image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-04-20T11:53:10.429343image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
0499
65.1%
1268
34.9%

Most occurring characters

ValueCountFrequency (%)
0499
65.1%
1268
34.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number767
100.0%

Most frequent character per category

ValueCountFrequency (%)
0499
65.1%
1268
34.9%

Most occurring scripts

ValueCountFrequency (%)
Common767
100.0%

Most frequent character per script

ValueCountFrequency (%)
0499
65.1%
1268
34.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII767
100.0%

Most frequent character per block

ValueCountFrequency (%)
0499
65.1%
1268
34.9%

Interactions

2021-04-20T11:52:59.151175image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:52:59.424497image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:52:59.560356image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:52:59.692516image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:52:59.834806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:52:59.981954image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:00.128098image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:00.266715image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:00.412534image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:00.558620image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:00.691273image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:00.824189image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:00.961642image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:01.108136image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:01.249289image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:01.381928image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:01.521269image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:01.650589image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:01.774134image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:01.893802image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:02.024770image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:02.259121image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:02.404753image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:02.537205image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:02.657801image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:02.777994image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:02.894350image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:03.018047image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:03.144012image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:03.283882image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:03.457999image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:03.586114image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:03.702010image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:03.815327image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:03.936209image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:04.066578image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:04.198136image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:04.343036image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:04.470901image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:04.588802image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:04.704077image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:04.831372image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:04.961496image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:05.095410image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:05.232101image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:05.364579image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:05.489690image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:05.612421image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:05.734589image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:05.862231image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:06.003098image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:06.154678image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:06.407462image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:06.544489image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:06.684799image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-20T11:53:06.818364image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-04-20T11:53:10.504822image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-20T11:53:10.734423image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-20T11:53:10.919970image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-20T11:53:11.154916image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-04-20T11:53:07.092156image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-20T11:53:07.348733image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
06148.072.00000035.00000079.9035233.6000000.627501
1185.066.00000029.00000079.9035226.6000000.351310
28183.064.00000020.52281679.9035223.3000000.672321
3189.066.00000023.00000094.0000028.1000000.167210
40137.040.00000035.000000168.0000043.1000002.288331
55116.074.00000020.52281679.9035225.6000000.201300
6378.050.00000032.00000088.0000031.0000000.248261
710115.069.10430220.52281679.9035235.3000000.134290
82197.070.00000045.000000543.0000030.5000000.158531
98125.096.00000020.52281679.9035231.9946540.232541

Last rows

PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
7570123.072.020.52281679.9035236.30.258521
7581106.076.020.52281679.9035237.50.197260
7596190.092.020.52281679.9035235.50.278661
760288.058.026.00000016.0000028.40.766220
7619170.074.031.00000079.9035244.00.403431
762989.062.020.52281679.9035222.50.142330
76310101.076.048.000000180.0000032.90.171630
7642122.070.027.00000079.9035236.80.340270
7655121.072.023.000000112.0000026.20.245300
7661126.060.020.52281679.9035230.10.349471